Automotive
Collision Avoidance System Field Operational Test
Program
FIRST ANNUAL REPORT

APPENDIX
A
Function Diagrams and Descriptions

This Appendix includes
the process model for the system that was developed as part
of the Functional Description Task (Task A1). The process
model shows the functional decomposition of the system
through data and control flow diagrams. Each circle in the
data and control flow diagrams represents a function
performed by the system. Double circles represent primitive
functions while single circles indicate there is another
diagram that decomposes the function into lower level
functions. The solid lines and arrows between the functions
indicate flow of information. The dashed lines indicate
control signals. Double horizontal lines with a name between
them represent data storage. Single vertical lines represent
flow into a control specification. Control specifications
define the states (operating modes) and state transitions of
the system.

The Context Diagram
(Figure A1) shows the relationship between the functions
provided by the system and the entities that interact with
the system. The ACC/FCW System takes inputs from sensors
that determine the driving environment, the driver’s
activities, the host vehicle actuators, and the vehicle
dynamics. The ACC/FCW system controls the vehicle speed when
ACC is active and produces Forward Collision Warning alerts
and warnings for the driver. The ACC maintains a constant
speed set by the driver or a set headway if there is a lead
vehicle that is going less than the set speed. The FCW
produces alerts and warnings based on an assessment of the
threat of a crash.

The ACC and FCW
Function diagram (Figure A2) shows the interaction between
the top-level functions and the entities that interact with
the system. The Sensor Specific Functions include radar
processing, vision-based lane tracking, map-based road
geometry estimation and yaw-based path estimation. These
functions use each sensor to determine the road geometry, to
estimate the current relationship between the vehicle and
the road, and/or to predict the host vehicle’s path. The
sensor specific functions also use the radar data to detect,
track and classify objects in the forward environment of the
host vehicle. Finally the sensor specific functions include
vehicle kinematics estimation based upon the GPS data.

The Vehicle
Sensor Filtering function filters the vehicle kinematics
sensors to provide engineering units and to reduce noise in
these measurements.

The
Data Fusion Functions combine the evidence from the
entire sensor suite to develop a higher confidence
prediction of the host vehicle’s path and to predict the
driver/vehicle response in the event of an alert.

The Target
Identification and Threat Assessment Functions determine
which targets are likely to cross the path of the host
vehicle, determine if a collision warning should be
produced, and select the targets for the ACC functions. They
also prioritize the targets to help with resource allocation
within the Sensor Specific Functions.

The ACC Vehicle
Controls maintain the vehicle’s speed or headway when
the ACC is on and engaged. The controls are similar to those
of a conventional cruise control system with the addition of
a headway setting. The output includes throttle and brake
actuator control signals. The ACC vehicle control also
responds to a brake pulse request by controlling the brake
actuator control signals. In headway maintenance mode the
ACC gets range and range rate data for the primary target
from the Target Selection function that is part of the
Threat Assessment Functions.

The Driver-Vehicle
Interface Functions control all of the devices that
transmit information to the driver. These include audio,
visual, and haptic outputs. The visual display includes a
head-up display. The information displayed includes the
status of the ACC (on, engaged, set speed, and target
detected). The information also includes warnings that
indicate maintenance is required or that the vehicle is
being operated beyond the range of capability of the ACC/FCW.
The warnings may include multiple levels.

The Data
Acquisition function includes the collection of data
from the FOT. These will include the vehicle kinematics,
warning levels, and intermediate results from many of the
processing functions. It will also include video of the
roadway ahead of the vehicle and the driver’s head.

The Vision-Based
Lane Tracking function determines the geometry of the
road ahead of the vehicle and the relationship between the
road and the host vehicle. The road-geometry information
includes the curvature and/or offset of the road at selected
distances ahead of the vehicle. The relationship between the
vehicle and the road includes the lateral position in the
lane, the heading angle, and whether a lane change is
occurring.

The Map-Based
Road Geometry Estimation function uses a roadmap
database, DGPS, and dead reckoning to determine the current
map position of the vehicle. It then extracts information
from the database indicating the geometry of the road ahead
of the vehicle, the relationship of the vehicle to the road,
and then the location of significant features along the
road. It also produces vehicle kinematics measurements based
upon the GPS data.

The Target
Detection function processes the radar signals to
produce estimates of the range, range rate, acceleration,
and extent of objects. It also reports the amplitude of the
return from each detection.

The Multi-Target
Tracking function associates detections in each new
sample with previously observed tracks. It reports whether
any currently stationary objects were ever observed to be
moving, and can let a target "coast" if it
disappears for a short period of time.

The Target
Classification function looks at the target tracks to
determine if any should be associated into a larger object
such as a bridge or a truck. If this occurs it indicates
which tracks are associated and calculates some composite
features of the object.

The Scene
Tracking function evaluates the target tracks to
estimate the geometry of the road ahead of the vehicle and
the vehicle’s relationship to the road.

The Auto-Alignment
and Blockage Detection function evaluates the radar
returns to detect when the signal seems to be attenuated by
a blocked radome. It also looks at target tracks to produce
electronic adjustments of the radar alignment. This function
also produces control signals that indicate if the radome is
blocked or if the alignment is beyond the range that can be
corrected.

Data fusion
techniques are used to combine results derived from
individual sensors into a composite evaluation of the road
geometry, host state, environment state and driver
distraction level (Figure A5), shown on following page.

The Host State
Estimation function uses data fusion techniques to
estimate the relationship between the host vehicle and the
road based upon the sensor specific estimates. This includes
determining if a lane change is occurring.

The Environment
State Estimation function uses data fusion techniques to
estimate the condition of the road, the weather, and the
visibility, based upon evidence from several vehicle
sensors.

The Driver
Distraction Level Estimation function keeps track of
driver activity to determine if the driver is performing
tasks other than driving. It uses this information to derive
an estimate of the distraction level of the driver.

The Target
Identification and Threat Assessment Functions (Figure
A6) identify targets that are likely to cross the host
vehicle’s path, estimate the driver’s and vehicle’s
response to each threat, determine if any of them satisfy
the criteria for FCW warnings, and selects the target for
ACC.

The Host Path
Prediction function uses the vehicle kinematics, road
geometry and host state to predict the path of the host
vehicle relative to its current position.

The Lane
Position Estimation function estimates the relationship
of each tracked target to the roadway geometry derived from
the tracks. It determines which lane the target is in, its
lateral offset, and its lateral velocity in that lane.

The TargetSelection
function evaluates the predicted path of the host
vehicle and the objects to determine the threatening targets
that will be used for ACC control and for FCW threat
assessment. The FCW targets are those that are in the host
vehicle’s path or are predicted to cross the host vehicle’s
path. They may be moving or stationary.

The Driver/Vehicle
Response Prediction function predicts how fast and how
hard a driver is likely to brake if a warning is generated.
It assesses the environmental conditions, current speed and
headway, and other driving conditions that impact reaction
time and the intensity of the response.

The Threat
Assessment function uses the host vehicle dynamics, the
target dynamics, and the expected driver response to
determine what level of warning should be generated. The
warning algorithm also depends upon whether the ACC is
active. When ACC is active a warning is produced if it is
predicted that the maximum braking authority will not
prevent a collision.

The Data
Acquisition Functions (Figure A7) record measured and
computed values as well as video and audio information. Most
variables are recorded continuously. Audio-visual data is
recorded in clips at regular intervals and when predefined
incidents are detected. The system transmits a summary of
the data to a base station at the end of each trip by the
host vehicle. The complete set of collected data is
offloaded when each subject is finished with the vehicle.

The Calculate
Derived Values function calculates values from the
directly measured values that may be required in real time
by other functions.

The Detect
Transitions and Episodes function looks for pre-defined
conditions that trigger storage of audio/video clips. The
count of some detected transitions and episodes may also be
stored and/or transmitted at the end of each trip.

The Update Time
History function maintains the log of continuously
recorded measurements and derived data.

The Update
Histograms and Counts functions maintain the histograms
of the measured values and counts of events that are
transmitted to the base station at the end of each trip.

The Cue
Audio-Video System is triggered by the detection of an
episode. It controls the software that logs audio and video
data for a short period before and after each episode. It
also causes the audio and video systems to record short
clips at regular intervals while the vehicle is operating.

The Digitize
Video function controls the frame grabber and collects
video from cameras that looks out the front window and at
the driver.

The Digitize
Audio function controls the audio digitizer for
recording sound from the passenger compartment.

The Store Frames
in Buffer and Store Audio in Buffers functions
control first-in first-out buffers so that data that
precedes the detection of an episode can be recorded.

The Log Selected
Video Buffers and Log Selected Audio Buffers
functions transfer data from the first-in first-out buffers
when triggered by the Cue Audio-Video System function.

Figures A8 and A9
show the relationship between the function diagrams and the
physical modules in the system. Figure A8 is an enhanced
version of the ACC and FCW Function. It augments the basic
functions with those required to control the interfaces. In
addition to the modules listed above, the enhanced
functional diagram shows the Brake, Throttle and HUD
modules.

The functions
performed by each module are enclosed in polygons on the
enhanced functional diagram and the subsequent
decompositions. Two functions in the top-level diagram have
sub-functions assigned to more than one module. Parts of the
Sensor Specific Functions are executed in the Radar, Vision
Module, Map-based Road Geometry Module, Path Prediction
& Target Selection, and Scene Tracking Modules. Parts of
the Threat Assessment Functions are executed in the Path
Prediction & Target Selection module and in the FCW
Processor. The assignment of each of the sub-functions to
each of the modules is shown in the subsequent diagrams.